The DOI matches its Crossref record; 61 of the 61 references it cites were found in a registry or an index.
Evolution of Integrated Causal Structures in Animats Exposed to Environments of Increasing Complexity
Open accessAbstract
Natural selection favors the evolution of brains that can capture fitness-relevant features of the environment's causal structure. We investigated the evolution of small, adaptive logic-gate networks ("animats") in task environments where falling blocks of different sizes have to be caught or avoided in a 'Tetris-like' game. Solving these tasks requires the integration of sensor inputs and memory. Evolved networks were evaluated using measures of information integration, including the number of evolved concepts and the total amount of integrated conceptual information. The results show that, over the course of the animats' adaptation, i) the number of concepts grows; ii) integrated conceptual information increases; iii) this increase depends on the complexity of the environment, especially on the requirement for sequential memory. These results suggest that the need to capture the causal structure of a rich environment, given limited sensors and internal mechanisms, is an important driving force for organisms to develop highly integrated networks ("brains") with many concepts, leading to an increase in their internal complexity.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source doesn’t support the claim as written.
In the citing paper
“Albantakis et al. (2014) analyzed networks with 3-6 nodes.”
From Can "consciousness" be observed from large language model (LLM) internal states? Dissecting LLM representations obtained from Theory of Mind test with Integrated Information Theory and Span Representation analysis (2025) · cites this paper as Albantakis et al., 2014
Checked by a reasoning model — may be wrong.
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Elements of Information Theory
Cover, T. M., & Thomas, J. A. (2001) · Wiley eBooks · doi:10.1002/0471200611 · Cited by 37,139
Multilayer feedforward networks are universal approximators
Hornik, K., Stinchcombe, M., & White, H. (1989) · Neural Networks · doi:10.1016/0893-6080(89)90020-8 · Cited by 21,728
Elements of Information Theory
Cover, T. M., & Thomas, J. A. (2005) · doi:10.1002/047174882x · Cited by 13,791
Causality: Models, Reasoning, and Inference
Pearl, J. (2009) · doi:10.1017/cbo9780511803161 · Cited by 11,653
Multilayer feedforward networks are universal approximators
HornikK., StinchcombeM., & WhiteH. (1989) · Neural Networks · doi:10.5555/70405.70408 · Cited by 9,266
Foxman, D., & Bateson, G. (1973) · The Western Political Quarterly · doi:10.2307/446833 · Cited by 7,263
The Earth Mover's Distance as a Metric for Image Retrieval
Rubner, Y., Tomasi, C., & Guibas, L. (2000) · International Journal of Computer Vision · doi:10.1023/a:1026543900054 · Cited by 5,547
CAUSALITY: MODELS, REASONING AND INFERENCE
Hitchcock, C., & Pearl, J. (2001) · The Philosophical Review · doi:10.2307/3182612 · Cited by 4,827
(1987) · Medical Entomology and Zoology · Cited by 2,512
Rich-club organization of the human connectome
van den Heuvel, M. P., & Sporns, O. (2011) · The Journal of Neuroscience · doi:10.1523/jneurosci.3539-11.2011 · Cited by 2,354
An information integration theory of consciousness
Tononi, G. (2004) · BMC Neuroscience · doi:10.1186/1471-2202-5-42 · Cited by 1,958 · cited 3×
“correct in claiming that the capacity for information integration underlies consciousness [14,15,18,23], finding an increase in animats’ W values in complex environments would provide a plausible account of why and how consciousness evolved.”
BackgroundBuilding high-level features using large scale unsupervised learning
Le, Q. V. (2013) · 2013 IEEE International Conference on Acoustics, Speech and Signal Processing · doi:10.1109/icassp.2013.6639343 · Cited by 1,695
Consciousness as integrated information: a provisional manifesto
Tononi, G. (2008) · The Biological Bulletin · doi:10.2307/25470707 · Cited by 1,529
Degeneracy and complexity in biological systems
Edelman, G. M., & Gally, J. A. (2001) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.231499798 · Cited by 1,493
From the phenomenology to the mechanisms of consciousness: integrated information theory 3.0
Oizumi, M., Albantakis, L., & Tononi, G. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003588 · Cited by 1,128
Self-citationSpontaneous evolution of modularity and network motifs
Kashtan, N., & Alon, U. S. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0503610102 · Cited by 885
Fast and robust Earth Mover's Distances
Pele, O., & Werman, M. (2009) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2009.5459199 · Cited by 865
The evolutionary origin of complex features
Lenski, R. E., Ofria, C., Pennock, R. T., & Adami, C. (2003) · Nature · doi:10.1038/nature01568 · Cited by 754
The Concept of Information in Biology
Smith, J. M. (2000) · Philosophy of Science · doi:10.1086/392768 · Cited by 733
Wiring optimization can relate neuronal structure and function
Chen, B. L., Hall, D. H., & Chklovskii, D. B. (2006) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0506806103 · Cited by 650
Measures of degeneracy and redundancy in biological networks
Tononi, G., Sporns, O., & Edelman, G. M. (1999) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.96.6.3257 · Cited by 632
Evolution of biological complexity
Adami, C., Ofria, C., & Collier, T. C. (2000) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.97.9.4463 · Cited by 592
ADAPTATION AND THE COST OF COMPLEXITY
Orr, H. A. (2000) · Evolution · doi:10.1111/j.0014-3820.2000.tb00002.x · Cited by 559
Predictability, Complexity, and Learning
Bialek, W., Nemenman, I. M., & Tishby, N. (2001) · Neural Computation · doi:10.1162/089976601753195969 · Cited by 524
The Dynamics of Active Categorical Perception in an Evolved Model Agent
Beer, R. D. (2003) · Adaptive Behavior · doi:10.1177/1059712303114001 · Cited by 505
The Evolution of Complexity by Means of Natural Selection
Bonner, J. T. (1988) · Princeton University Press eBooks · doi:10.1515/9780691222110 · Cited by 493
The Mindful Brain: Cortical Organization and the Group-Selective Theory of Higher Brain Function
Edelman, G. M., & Mountcastle, V. B. (1978) · Medical Entomology and Zoology · Cited by 435
Integrated information in discrete dynamical systems: motivation and theoretical framework
Balduzzi, D., & Tononi, G. (2008) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000091 · Cited by 419
Self-citationAdami, C. (2002) · BioEssays · doi:10.1002/bies.10192 · Cited by 397
Quantifying causal emergence shows that macro can beat micro
Hoel, E. P., Albantakis, L., & Tononi, G. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1314922110 · Cited by 378
Integrated information theory of consciousness: an updated account.
(2012) · PubMed · doi:10.4449/aib.v149i5.1388 · Cited by 370
The Evolution of Complexity by Means of Natural Selection
Bonner, J. T. (2020) · Princeton University Press eBooks · doi:10.2307/j.ctv173f26c · Cited by 368
PERSPECTIVE METAZOAN COMPLEXITY AND EVOLUTION: IS THERE A TREND?
McShea, D. W. (1996) · Evolution · doi:10.1111/j.1558-5646.1996.tb03861.x · Cited by 353
Building high-level features using large scale unsupervised learningPreprint
Le, Q. V., Ranzato, M., Monga, R., Devin, M., Chen, K., Corrado, G. S., Dean, J., & Ng, A. Y. (2011) · arXiv (Cornell University) · doi:10.48550/arxiv.1112.6209 · Cited by 341
The Concept of Information in Biology
Smith, J. M. (2002) · Springer eBooks · doi:10.1007/978-94-017-0475-5_18 · Cited by 333
A Linear Time Histogram Metric for Improved SIFT Matching
Pele, O., & Werman, M. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-88690-7_37 · Cited by 309
Measuring information integration
Tononi, G., & Sporns, O. (2003) · BMC Neuroscience · doi:10.1186/1471-2202-4-31 · Cited by 294
Self-citationQualia: The Geometry of Integrated Information
Balduzzi, D., & Tononi, G. (2009) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000462 · Cited by 269
Self-citationAn information-theoretic primer on complexity, self-organization, and emergence
Prokopenko, M., Boschetti, F., & Ryan, A. J. (2009) · Complexity · doi:10.1002/cplx.20249 · Cited by 251
Information: Its Interpretation, Its Inheritance, and Its Sharing
Jablonka, E. (2002) · Philosophy of Science · doi:10.1086/344621 · Cited by 247
Metazoan Complexity and Evolution: Is There a Trend?Preprint
McShea, D. W. (1996) · RePEc: Research Papers in Economics · Cited by 226
Information-theoretic approach to the study of control systems
Touchette, H., & Lloyd, S. (2003) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2003.09.007 · Cited by 202
A complexity measure for selective matching of signals by the brain.
Tononi, G., Sporns, O., & Edelman, G. M. (1996) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.93.8.3422 · Cited by 191
Evolution of Complex Modular Biological Networks
Hintze, A., & Adami, C. (2005) · PLoS Computational Biology · doi:10.1371/journal.pcbi.0040023 · Cited by 190
The Value of Information for Populations in Varying Environments
Rivoire, O., & Leibler, S. (2011) · Journal of Statistical Physics · doi:10.1007/s10955-011-0166-2 · Cited by 171
Evolution and development in cave animals: from fish to crustaceans
Protas, M. E., & Jeffery, W. R. (2012) · Wiley Interdisciplinary Reviews Developmental Biology · doi:10.1002/wdev.61 · Cited by 166
Predictive information and explorative behavior of autonomous robots
Ay, N., Bertschinger, N., Der, R., Güttler, F., & Olbrich, E. (2008) · The European Physical Journal B · doi:10.1140/epjb/e2008-00175-0 · Cited by 165
Calvin, W. H. (1988) · Science · doi:10.1126/science.240.4860.1802 · Cited by 151
Termite Nests—A Study of the Phylogeny of Behavior
Emerson, A. E. (1938) · Ecological Monographs · doi:10.2307/1943251 · Cited by 146
Integrated Information Increases with Fitness in the Evolution of Animats
Edlund, J. A., Chaumont, N., Hintze, A., Koch, C., Tononi, G., & Adami, C. (2011) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002236 · Cited by 126
ADAPTATION AND THE COST OF COMPLEXITY
Orr, H. A. (2000) · Evolution · doi:10.1554/0014-3820(2000)054[0013:aatcoc]2.0.co;2 · Cited by 117
The use of information theory in evolutionary biology
Adami, C. (2012) · Annals of the New York Academy of Sciences · doi:10.1111/j.1749-6632.2011.06422.x · Cited by 113
Information: Currency of life?
Polani, D. (2009) · HFSP journal · doi:10.2976/1.3171566 · Cited by 87
Cave Vertebrates of America: A Study in Degenerative Evolution
Eigenmann, C. H. (2010) · Biodiversity Heritage Library (Smithsonian Institution) · doi:10.5962/bhl.title.4078 · Cited by 86
The Evolution of Representation in Simple Cognitive Networks
Marstaller, L., Hintze, A., & Adami, C. (2013) · Neural Computation · doi:10.1162/neco_a_00475 · Cited by 72
(2001) · PubMed · Cited by 39
EXTINCTIONS IN HETEROGENEOUS ENVIRONMENTS AND THE EVOLUTION OF MODULARITY
Kashtan, N., Parter, M., Dekel, E., Mayo, A., & Alon, U. S. (2009) · Evolution · doi:10.1111/j.1558-5646.2009.00684.x · Cited by 39
Information and fitnessPreprint
Taylor, S. F., Tishby, N., & Bialek, W. (2007) · arXiv (Cornell University) · doi:10.48550/arxiv.0712.4382 · Cited by 37
The Minimal Complexity of Adapting Agents Increases with Fitness
Joshi, N. J., Tononi, G., & Koch, C. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003111 · Cited by 33
Evolution of Neural Structure and Complexity in a Computational Ecology
Yaeger, L., & Sporns, O. (2006) · Cited by 24
Cave vertebrates of America; a study in degenerative evolution, by Carl H. Eigenmann.
Eigenmann, C. H. (1909) · Carnegie Institution of Washington eBooks · doi:10.5962/bhl.title.11955 · Cited by 13
Flagged first, then by citations
Research works printed but not linked: 1
- Bateson G (1972) Steps to an Ecology of Mind. University of Chicago Press.
Cited by
Sorted by citations
Integrated information theory: from consciousness to its physical substrate
Tononi, G., Boly, M., Massimini, M., & Koch, C. (2016) · Nature Reviews Neuroscience · doi:10.1038/nrn.2016.44 · Cited by 1,727
Seth, A. K., & Bayne, T. (2022) · Nature Reviews Neuroscience · doi:10.1038/s41583-022-00587-4 · Cited by 880
Consciousness: here, there and everywhere?
Tononi, G., & Koch, C. (2015) · Philosophical Transactions of the Royal Society B: Biological Sciences · doi:10.1098/rstb.2014.0167 · Cited by 811
Albantakis, L., Barbosa, L., Findlay, G., Grasso, M., Haun, A. M., Marshall, W., Mayner, W. G. P., Zaeemzadeh, A., Boly, M., Juel, B. E., et al. (2023) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1011465 · Cited by 292
“Yet another is the study of the evolution of a substrate’s causal powers as conditional probabilities that update themselves [74].”
Can the macro beat the micro? Integrated information across spatiotemporal scales
Hoel, E. P., Albantakis, L., Marshall, W., & Tononi, G. (2016) · Neuroscience of Consciousness · doi:10.1093/nc/niw012 · Cited by 140
Self-citation
Metadata
- Authors
- Albantakis, L., Hintze, A., Koch, C., Adami, C., & Tononi, G.
- Affiliations
- University of Wisconsin–Madison (US)
- Michigan State University (US)
- Corresponding author
- Albantakis, Larissa
- Tononi, Giulio
- Venue
- PLoS Computational Biology
- Publisher
- Public Library of Science (PLoS)
- Published
- 2014-12-18
- Volume
- 10
- Issue
- 12
- Pages
- e1003966
- ISSN
- 1553-734X (ISSN-L)
- 1553-7358
- PMC
- PMC4270440
- OpenAlex
- W2070484080
- Cited by
- 101
- References
- 61
- Citations by year
- 2026: 8 · 2025: 7 · 2024: 10 · 2023: 5 · 2022: 8 · 2021: 12 · 2020: 10 · 2019: 12 · 2018: 10 · 2017: 8 · 2016: 6 · 2015: 5
- FWCI
- 6.31
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- PLoS Computational Biology (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Diva portal (Dalarna University Library) (submittedVersion, other-oa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion, cc-by)
- Funders
- Paul G. Allen Family Foundation
- Templeton World Charity Foundation
- Defense Advanced Research Projects Agency
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Evolutionary Algorithms and Applications
- Life Sciences › Neuroscience › Cognitive Neuroscience › Embodied and Extended Cognition
- Physical Sciences › Computer Science › Artificial Intelligence › Psychiatry, Mental Health, Neuroscience
- Keywords
- animats, causal structure, information integration, conceptual information, environmental complexity, sequential memory, sensor integration, cognitive evolution, internal complexity
- MeSH terms
- Adaptation, Physiological, Algorithms, Computer Simulation, Biological Evolution, Models, Neurological, Selection, Genetic, Statistics, Nonparametric, Computational Biology, Feedback, Physiological, Genetic Fitness
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
